Preface xiii 1 Bioinfomatics as a Tool in Drug Designing 1Rene Barbie Browne, Shiny C. Thomas and Jayanti Datta Roy 1.1 Introduction 1 1.2 Steps Involved in Drug Designing 3 1.2.1 Identification of the Target Protein/Enzyme 5 1.2.2 Detection of Molecular Site (Active Site) in the Target Protein 6 1.2.3 Molecular Modeling 6 1.2.4 Virtual Screening 9 1.2.5 Molecular Docking 10 1.2.6 QSAR (Quantitative Structure-Activity Relationship) 12 1.2.7 Pharmacophore Modeling 14 1.2.8 Solubility of Molecule 14 1.2.9 Molecular Dynamic Simulation 14 1.2.10 ADME Prediction 15 1.3 Various Softwares Used in the Steps of Drug Designing 16 1.4 Applications 18 1.5 Conclusion 20 References 20 2 New Strategies in Drug Discovery 25Vivek Chavda, Yogita Thalkari and Swati Marwadi 2.1 Introduction 26 2.2 Road Toward Advancement 27 2.3 Methodology 30 2.3.1 Target Identification 30 2.3.2 Docking-Based Virtual Screening 32 2.3.3 Conformation Sampling 33 2.3.4 Scoring Function 34 2.3.5 Molecular Similarity Methods 35 2.3.6 Virtual Library Construction 37 2.3.7 Sequence-Based Drug Design 37 2.4 Role of OMICS Technology 38 2.5 High-Throughput Screening and Its Tools 40 2.6 Chemoinformatic 44 2.6.1 Exploratory Data Analysis 45 2.6.2 Example Discovery 46 2.6.3 Pattern Explanation 46 2.6.4 New Technologies 46 2.7 Concluding Remarks and Future Prospects 46 References 48 3 Role of Bioinformatics in Early Drug Discovery: An Overview and Perspective 49Shasank S. Swain and Tahziba Hussain 3.1 Introduction 50 3.2 Bioinformatics and Drug Discovery 51 3.2.1 Structure-Based Drug Design (SBDD) 52 3.2.2 Ligand-Based Drug Design (LBDD) 53 3.3 Bioinformatics Tools in Early Drug Discovery 54 3.3.1 Possible Biological Activity Prediction Tools 55 3.3.2 Possible Physicochemical and Drug-Likeness Properties Verification Tools 58 3.3.3 Possible Toxicity and ADME/T Profile Prediction Tools 60 3.4 Future Directions With Bioinformatics Tool 61 3.5 Conclusion 63 Acknowledgements 64 References 64 4 Role of Data Mining in Bioinformatics 69Vivek P. Chavda, Amit Sorathiya, Disha Valu and Swati Marwadi 4.1 Introduction 70 4.2 Data Mining Methods/Techniques 71 4.2.1 Classification 71 4.2.1.1 Statistical Techniques 71 4.2.1.2 Clustering Technique 73 4.2.1.3 Visualization 74 4.2.1.4 Induction Decision Tree Technique 74 4.2.1.5 Neural Network 75 4.2.1.6 Association Rule Technique 75 4.2.1.7 Classification 75 4.3 DNA Data Analysis 77 4.4 RNA Data Analysis 79 4.5 Protein Data Analysis 79 4.6 Biomedical Data Analysis 80 4.7 Conclusion and Future Prospects 81 References 81 5 In Silico Protein Design and Virtual Screening 85Vivek P. Chavda, Zeel Patel, Yashti Parmar and Disha Chavda 5.1 Introduction 86 5.2 Virtual Screening Process 88 5.2.1 Before Virtual Screening 90 5.2.2 General Process of Virtual Screening 90 5.2.2.1 Step 1 (The Establishment of the Receptor Model) 91 5.2.2.2 Step 2 (The Generation of Small-Molecule Libraries) 92 5.2.2.3 Step 3 (Molecular Docking) 92 5.2.2.4 Step 4 (Selection of Lead Protein Compounds) 94 5.3 Machine Learning and Scoring Functions 94 5.4 Conclusion and Future Prospects 95 References 96 6 New Bioinformatics Platform-Based Approach for Drug Design 101Vivek Chavda, Soham Sheta, Divyesh Changani and Disha Chavda 6.1 Introduction 102 6.2 Platform-Based Approach and Regulatory Perspective 104 6.3 Bioinformatics Tools and Computer-Aided Drug Design 107 6.4 Target Identification 109 6.5 Target Validation 110 6.6 Lead Identification and Optimization 111 6.7 High-Throughput Methods (HTM) 112 6.8 Conclusion and Future Prospects 114 References 115 7 Bioinformatics and Its Application Areas 121Ragini Bhardwaj, Mohit Sharma and Nikhil Agrawal 7.1 Introduction 121 7.2 Review of Bioinformatics 124 7.3 Bioinformatics Applications in Different Areas 126 7.3.1 Microbial Genome Application 126 7.3.2 Molecular Medicine 129 7.3.3 Agriculture 130 7.4 Conclusion 131 References 131 8 DNA Microarray Analysis: From Affymetrix CEL Files to Comparative Gene Expression 139Sandeep Kumar, Shruti Shandilya, Suman Kapila, Mohit Sharma and Nikhil Agrawal 8.1 Introduction 140 8.2 Data Processing 140 8.2.1 Installation of Workflow 140 8.2.2 Importing the Raw Data for Processing 141 8.2.3 Retrieving Sample Annotation of the Data 142 8.2.4 Quality Control 143 8.2.4.1 Boxplot 144 8.2.4.2 Density Histogram 145 8.2.4.3 MA Plot 145 8.2.4.4 NUSE Plot 145 8.2.4.5 RLE Plot 145 8.2.4.6 RNA Degradation Plot 145 8.2.4.7 QCstat 148 8.3 Normalization of Microarray Data Using the RMA Method 148 8.3.1 Background Correction 148 8.3.2 Normalization 149 8.3.3 Summarization 149 8.4 Statistical Analysis for Differential Gene Expression 151 8.5 Conclusion 153 References 153 9 Machine Learning in Bioinformatics 155Rahul Yadav, Mohit Sharma and Nikhil Agrawal 9.1 Introduction and Background 156 9.1.1 Bioinformatics 158 9.1.2 Text Mining 159 9.1.3 IoT Devices 159 9.2 Machine Learning Applications in Bioinformatics 159 9.3 Machine Learning Approaches 161 9.4 Conclusion and Closing Remarks 162 References 162 10 DNA-RNA Barcoding and Gene Sequencing 165Gifty Sawhney, Mohit Sharma and Nikhil Agrawal 10.1 Introduction 166 10.2 RNA 169 10.3 DNA Barcoding 172 10.3.1 Introduction 172 10.3.2 DNA Barcoding and Molecular Phylogeny 177 10.3.3 Ribosomal DNA (rDNA) of the Nuclear Genome (nuDNA)—ITS 178 10.3.4 Chloroplast DNA 180 10.3.5 Mitochondrial DNA 181 10.3.6 Molecular Phylogenetic Analysis 181 10.3.7 Metabarcoding 189 10.3.8 Materials for DNA Barcoding 190 10.4 Main Reasons of DNA Barcoding 191 10.5 Limitations/Restrictions of DNA Barcoding 192 10.6 RNA Barcoding 192 10.6.1 Overview of the Method 193 10.7 Methodology 194 10.7.1 Materials Required 195 10.7.2 Barcoded RNA Sequencing High-Level Mapping of Single-Neuron Projections 196 10.7.3 Using RNA to Trace Neurons 196 10.7.4 A Life Conservation Barcoder 198 10.7.5 Gene Sequencing 199 10.7.5.1 DNA Sequencing Methods 200 10.7.5.2 First-Generation Sequencing Techniques 204 10.7.5.3 Maxam’s and Gilbert’s Chemical Method 204 10.7.5.4 Sanger Sequencing 205 10.7.5.5 Automation in DNA Sequencing 206 10.7.5.6 Use of Fluorescent-Marked Primers and ddNTPs 206 10.7.5.7 Dye Terminator Sequencing 207 10.7.5.8 Using Capillary Electrophoresis 207 10.7.6 Developments and High-Throughput Methods in DNA Sequencing 208 10.7.7 Pyrosequencing Method 209 10.7.8 The Genome Sequencer 454 FLX System 210 10.7.9 Illumina/Solexa Genome Analyzer 210 10.7.10 Transition Sequencing Techniques 211 10.7.11 Ion-Torrent’s Semiconductor Sequencing 211 10.7.12 Helico’s Genetic Analysis Platform 211 10.7.13 Third-Generation Sequencing Techniques 212 10.8 Conclusion 212 Abbreviations 213 Acknowledgement 214 References 214 11 Bioinformatics in Cancer Detection 229Mohit Sharma, Umme Abiha, Parul Chugh, Balakumar Chandrasekaran and Nikhil Agrawal 11.1 Introduction 230 11.2 The Era of Bioinformatics in Cancer 230 11.3 Aid in Cancer Research via NCI 232 11.4 Application of Big Data in Developing Precision Medicine 233 11.5 Historical Perspective and Development 235 11.6 Bioinformatics-Based Approaches in the Study of Cancer 237 11.6.1 SLAMS 237 11.6.2 Module Maps 238 11.6.3 COPA 239 11.7 Conclusion and Future Challenges 240 References 240 12 Genomic Association of Polycystic Ovarian Syndrome: Single-Nucleotide Polymorphisms and Their Role in Disease Progression 245Gowtham Kumar Subbaraj and Sindhu Varghese 12.1 Introduction 246 12.2 FSHR Gene 252 12.3 IL-10 Gene 252 12.4 IRS-1 Gene 253 12.5 PCR Primers Used 254 12.6 Statistical Analysis 255 12.7 Conclusion 258 References 259 13 An Insight of Protein Structure Predictions Using Homology Modeling 265S. Muthumanickam, P. Boomi, R. Subashkumar, S. Palanisamy, A. Sudha, K. Anand, C. Balakumar, M. Saravanan, G. Poorani, Yao Wang, K. Vijayakumar and M. Syed Ali 13.1 Introduction 266 13.2 Homology Modeling Approach 268 13.2.1 Strategies for Homology Modeling 269 13.2.2 Procedure 269 13.3 Steps Involved in Homology Modeling 270 13.3.1 Template Identification 270 13.3.2 Sequence Alignment 271 13.3.3 Backbone Generation 271 13.3.4 Loop Modeling 271 13.3.5 Side Chain Modeling 272 13.3.6 Model Optimization 272 13.3.6.1 Model Validation 272 13.4 Tools Used for Homology Modeling 273 13.4.1 Robetta 273 13.4.2 M4T (Multiple Templates) 273 13.4.3 I-Tasser (Iterative Implementation of the Threading Assembly Refinement) 273 13.4.4 ModBase 274 13.4.5 Swiss Model 274 13.4.6 PHYRE2 (Protein Homology/Analogy Recognition Engine 2) 274 13.4.7 Modeller 274 13.4.8 Conclusion 275 Acknowledgement 275 References 275 14 Basic Concepts in Proteomics and Applications 279Jesudass Joseph Sahayarayan, A.S. Enogochitra and Murugesan Chandrasekaran 14.1 Introduction 280 14.2 Challenges on Proteomics 281 14.3 Proteomics Based on Gel 283 14.4 Non-Gel–Based Electrophoresis Method 284 14.5 Chromatography 284 14.6 Proteomics Based on Peptides 285 14.7 Stable Isotopic Labeling 286 14.8 Data Mining and Informatics 287 14.9 Applications of Proteomics 289 14.10 Future Scope 290 14.11 Conclusion 291 References 292 15 Prospects of Covalent Approaches in Drug Discovery: An Overview 295Balajee Ramachandran, Saravanan Muthupandian and Jeyakanthan Jeyaraman 15.1 Introduction 296 15.2 Covalent Inhibitors Against the Biological Target 297 15.3 Application of Physical Chemistry Concepts in Drug Designing 299 15.4 Docking Methodologies—An Overview 301 15.5 Importance of Covalent Targets 302 15.6 Recent Framework on the Existing Docking Protocols 303 15.7 SN2 Reactions in the Computational Approaches 304 15.8 Other Crucial Factors to Consider in the Covalent Docking 305 15.8.1 Role of Ionizable Residues 305 15.8.2 Charge Regulation 306 15.8.3 Charge-Charge Interactions 306 15.9 QM/MM Approaches 309 15.10 Conclusion and Remarks 310 Acknowledgements 311 References 311 Index 321

Bioinformatics is a platform between the biology and information technology and this book provides readers with an understanding of the use of bioinformatics tools in new drug design. The discovery of new solutions to pandemics is facilitated through the use of promising bioinformatics techniques and integrated approaches. This book covers a broad spectrum of the bioinformatics field, starting with the basic principles, concepts, and application areas. Also covered is the role of bioinformatics in drug design and discovery, including aspects of molecular modeling. Some of the chapters provide detailed information on bioinformatics related topics, such as silicon design, protein modeling, DNA microarray analysis, DNA-RNA barcoding, and gene sequencing, all of which are currently needed in the industry. Also included are specialized topics, such as bioinformatics in cancer detection, genomics, and proteomics. Moreover, a few chapters explain highly advanced topics, like machine learning and covalent approaches to drug design and discovery, all of which are significant in pharma and biotech research and development. Audience Researchers and engineers in computation biology, information technology, bioinformatics, drug design, biotechnology, pharmaceutical sciences.